Systems and methods for developing sizing of a pipe network

Abstract

Systems and methods for developing and testing pipe network sizing are provided. A framework can be used that takes into consideration design parameters, which can include, for example, relative wealth (e.g., high, medium, low), regional location, expected number of occupants, and whatever other parameters are deemed as of interest and / or necessary. Each set of parameters can be used to determine the distributions that are used for the fixtures in a simulation. Each fixture can have a distribution for duration and frequency of use, and those distributions can vary based on the time of day. Then, a layout of the system can be developed, and a designer can draw the network of pipes in isometrically, as well as carry out pressure loss calculations and branch by branch pipe sizing of the designed pipe network.

Description

[0001] DESCRIPTION

[0002] SYSTEMS AND METHODS FOR DEVELOPING SIZING OF A PIPE NETWORK

[0003] CROSS-REFERENCE TO RELATED APPLICATION

[0004] This application claims the benefit of U.S. Provisional Application Serial No. 63 / 708,535, filed October 17, 2024, the disclosure of which is hereby incorporated by reference in its entirety, including all figures, tables, and drawings.

[0005] BACKGROUND

[0006] When a structure is built, the piping system(s) within the structure must be sized, as is the case if / when the piping system(s) within a structure is / are rebuilt. If such piping system(s) are sized incorrectly, it could cause excessive pressure losses, detrimental health impacts on residents / users of the structure, and / or other problems.

[0007] BRIEF SUMMARY

[0008] Embodiments of the subject invention provide novel and advantageous systems and methods for developing and testing pipe network sizing. A framework can be used that takes into consideration design parameters, which can include, for example, relative wealth (e.g., high, medium, low), regional location, expected number of occupants, and whatever other parameters are deemed as of interest and / or necessary. Each set of parameters can be used to determine the distributions to characterize use events for the fixtures (e.g., fixtures in a structure that use piping, such as sink, shower, etc.) in a simulation. Each fixture can have a distribution for duration and frequency of use, as well as distributions related to start time and portion of hot and cold water, and those distributions can vary based on the time of day (e.g., which can simulate the behavioral patterns of going to work or staying home). Then, a layout of the system can be developed utilizing artificial intelligence (Al) image reading tools to read floor plans and construction documents to recognize the locations of all fixtures within the structure and / or development. Designers can draw the network of pipes in isometrically.

[0009] In an embodiment, a system for developing a floor plan (and / or sizing plan) of a pipe network comprising a plurality of components can comprise: a processor; and a (non- transitory) machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following

[0010] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr steps: a) receiving (e.g., from a user / designer) or determining edges and nodes within the pipe network; b) selecting distributions representing behavior of fixtures within the pipe network based on predetermined design criteria for the pipe network; c) generating a set of events by randomly sampling from frequency distributions of the fixtures using a Monte Carlo simulation; d) characterizing each event of the set of events by independently sampling behavior distributions of the fixtures to determine a duration parameter, a hot water ratio parameter, and a start time parameter (each of the parameters, including a frequency parameter, can vary according to influential factors / parameters); e) applying the set of events to the pipe network, wherein, for each event of the set of events, a flow is routed through the pipe network to generate an expected flow rate for each component of the plurality of components for a given timestamp in the Monte Carlo simulation; f) recording a maximum flow rate for each component of the plurality of components after all events of the set of events are applied / assigned; g) repeating steps b) - f) for a predetermined number of trials; h) upon completion of the predetermined number of trials, compiling the maximum flow rate for each component of the plurality of components; and i) selecting, as the respective design flow for each component of the plurality of components, the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, thereby generating the floor plan (and / or sizing plan) for the pipe network. In some cases, step i) can alternatively be: i) selecting, as the respective design flow for each component of the plurality of components, an average of the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, thereby generating the floor plan (and / or sizing plan) for the pipe network. The instructions when executed can further perform the following step: j) upon completion of the predetermined number of trials, compiling a total cumulative water consumption for the pipe network (this can be used as an alternative to generate the floor plan (and / or sizing plan) for the pipe network instead of the maximum flow rate for each component, such that step j) can be done instead of steps h) and i)). The pipe network can be drawn (e.g., by the designer and / or design engineer) isometrically using the floor plan (and / or sizing plan) for the pipe network The drawn pipe network can be used to design and / or build the pipe network. The first function can be a quantile function and / or a cumulative distribution function (CDF). The instructions when executed can further perform the following steps: d-1) determining a number of events for each fixture given parameters (e.g., a duration parameter, a hot water ratio parameter, a start time parameter, and / or a frequency parameter) for a single day; d-2) determining the duration parameter, the hot water

[0011] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr ratio parameter, and the start time parameter for each event from step d-1); d-3) upon events being available to be assigned, assigning the events from step d-1) to the nodes (e.g., fixtures); d-4) upon no nodes (or events) being available to be assigned, regenerating a new start time (randomly or by waiting until the previous event is finished), and then reassigning the events from step d-1) to the nodes until each node has an event assigned thereto; e-1) upon all events being assigned, applying each event to upstream pipes and updating a flow rate for each component for each event; g-1) upon completion of step e-1), starting a next trial using the maximum flow rate for each component; and / or i-1) upon completion of the predetermined number of trials, utilizing the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, or fitting a distribution to an achieved flow of the pipe network, and then selecting an exceedance probability to determine the respective design flow for each component. A plurality of influential parameters can be used to determine the duration of use (i.e., duration parameter), the ratio of hot water to cold water (i.e., hot water ratio parameter, the start time parameter, and / or the frequency of use (i.e., frequency parameter) for each active fixture in the pipe network. The plurality of parameters can comprise at least one of: relative wealth of an area having a structure having the pipe network; regional location of the structure; square footage of the structure; number of rooms of the structure; number of bathrooms of the structure; seasonal variations of the area having the structure; a size of a lot on which the structure is located; a cost of water in the area having the structure; ages of (expected) occupants within the structure; number of (expected) occupants within the structure; and composition of (expected) occupants within the structure. The system can further comprise a display in operable communication with the processor and / or the machine-readable medium. The instructions when executed can further perform the step of displaying on the display: the floor plan (and / or sizing plan) for the pipe network (which can include pressure losses across the pipe network and / or branch by branch pipe sizing computations); the duration of use (i.e., the duration parameter) for each active fixture; the percentage of hot water and the percentage of cold water within the pipe network (i.e., the hot water ratio parameter); the current flowrate for each edge; the layout of the structure; the start time for each active fixture (i.e., the start time parameter); and / or the frequency of use for each active fixture (i.e., the frequency parameter).

[0012] In another embodiment, a method for developing a floor plan (and / or sizing plan) of a pipe network comprising a plurality of components can comprise: a) determining (e.g., by a user / designer or by a processor) edges and nodes within the pipe network; b) selecting

[0013] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr distributions representing behavior of fixtures within the pipe network based on predetermined design criteria for the pipe network; c) generating a set of events by randomly sampling from frequency distributions of the fixtures using a Monte Carlo simulation; d) characterizing each event of the set of events by independently sampling behavior distributions of the fixtures to determine a duration parameter, a hot water ratio parameter, and a start time parameter (each of the parameters, including a frequency parameter, can vary according to influential factors / parameters); e) applying the set of events to the pipe network, wherein, for each event of the set of events, a flow is routed through the pipe network to generate an expected flow rate for each component of the plurality of components for a given timestamp in the Monte Carlo simulation; f) recording a maximum flow rate for each component of the plurality of components after all events of the set of events are applied / assigned; g) repeating steps b) - f) for a predetermined number of trials; h) upon completion of the predetermined number of trials, compiling the maximum flow rate for each component of the plurality of components; and i) selecting, as the respective design flow for each component of the plurality of components, the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, thereby generating the floor plan (and / or sizing plan) for the pipe network. In some cases, step i) can alternatively be: i) selecting, as the respective design flow for each component of the plurality of components, an average of the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, thereby generating the floor plan (and / or sizing plan) for the pipe network. The method can further comprise the following step: j) upon completion of the predetermined number of trials, compiling a total cumulative water consumption for the pipe network (this can be used as an alternative to generate the floor plan (and / or sizing plan) for the pipe network instead of the maximum flow rate for each component, such that step j) can be done instead of steps h) and i)). The method can further comprise: k) drawing the pipe network (e.g., by the designer and / or design engineer) isometrically using the floor plan (and / or sizing plan) for the pipe network; 1) carrying out (e.g., by the designer and / or design engineer) pressure loss calculations, and / or computations of sizing of pipes in the pipe network (e.g., branch by branch computations for all branches in the pipe network), across the designed / drawn pipe network; and / or 1) using the drawn pipe network to design and / or build the pipe network. The first function can be a quantile function and / or a cumulative distribution function (CDF). The method can further comprise the following steps: d-1) determining a number of events for each fixture given parameters (e.g., a duration parameter, a hot water ratio parameter, a start time

[0014] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr parameter, and / or a frequency parameter) for a single day; d-2) determining the duration parameter, the hot water ratio parameter, and the start time parameter for each event from step d-1); d-3) upon events being available to be assigned, assigning the events from step d-1) to the nodes (e.g., fixtures); d-4) upon no nodes (or events) being available to be assigned, regenerating a new start time (randomly or by waiting until the previous event is finished), and then reassigning the events from step d-1) to the nodes until each node has an event assigned thereto; e-1) upon all events being assigned, applying each event to upstream pipes and updating a flow rate for each component for each event; g-1) upon completion of step e-1), starting a next trial using the maximum flow rate for each component; and / or i-1) upon completion of the predetermined number of trials, utilizing the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, or fitting a distribution to an achieved flow of the pipe network, and then selecting an exceedance probability to determine the respective design flow for each component. A plurality of influential parameters can be used to determine the duration of use (i.e., duration parameter), the ratio of hot water to cold water (i.e., hot water ratio parameter, the start time parameter, and / or the frequency of use (i.e., frequency parameter) for each active fixture in the pipe network. The plurality of parameters can comprise at least one of: relative wealth of an area having a structure having the pipe network; regional location of the structure; square footage of the structure; number of rooms of the structure; number of bathrooms of the structure; seasonal variations of the area having the structure; a size of a lot on which the structure is located; a cost of water in the area having the structure; ages of (expected) occupants within the structure; number of (expected) occupants within the structure; and composition of (expected) occupants within the structure. The system can further comprise a display in operable communication with the processor and / or the machine-readable medium. The method can further comprise displaying on the display: the floor plan (and / or sizing plan) for the pipe network (which can include pressure losses across the pipe network); the duration of use (i.e., the duration parameter) for each active fixture; the percentage of hot water and the percentage of cold water within the pipe network (i.e., the hot water ratio parameter); the current flowrate for each edge; the layout of the structure; the start time for each active fixture (i.e., the start time parameter); and / or the frequency of use for each active fixture (i.e., the frequency parameter). Any or all steps or sub-steps can be performed by at least one processor.

[0015] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 shows a screenshot of a user interface (UI) of a system, according to an embodiment of the subject invention.

[0017] Figure 2 shows a screenshot of a UI of a system, according to an embodiment of the subject invention.

[0018] Figure 3 shows a schematic view of a setup on a structure level, according to an embodiment of the subject invention.

[0019] Figure 4 shows a flow diagram of fixtures within a structure, according to an embodiment of the subject invention.

[0020] Figure 5 shows a flow diagram of a simulation that was run using the fixtures shown in Figure 4.

[0021] Figure 6 shows results of the simulation depicted in Figure 5.

[0022] Figure 7 shows a flow chart of a system for developing and testing pipe network sizing, according to an embodiment of the subject invention.

[0023] DETAILED DESCRIPTION

[0024] Embodiments of the subject invention provide novel and advantageous systems and methods for developing and testing pipe network sizing. A framework can be used that takes into consideration design parameters, which can include, for example, relative wealth (e.g., high, medium, low), regional location, expected number of occupants, ages of (expected) occupants, composition of (expected) occupants, and whatever other parameters are deemed as of interest and / or necessary. Each set of parameters can be used to determine the distributions to characterize use events for the fixtures (e.g., fixtures in a structure that use piping, such as sink, shower, etc.) in a simulation. Each fixture can have a distribution for duration and frequency of use, as well as distributions related to start time and portion of hot and cold water, and those distributions can vary based on the time of day (e.g., which can simulate the behavioral patterns of going to work or staying home). Then, a layout of the system can be developed utilizing artificial intelligence (Al) image reading tools to read floor plans and construction documents to recognize the locations of all fixtures within the structure and / or development. Designers can draw the network of pipes in isometrically and / or can carry out pressure loss and / or sizing calculations across the designed pipe network.

[0025] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr In some embodiments, a simulation (e.g., a Monte Carlo simulation) can be used to predict the peak demand and / or total daily consumption by randomly generating numbers based on the distributions discussed in the previous paragraph. If a fixture is active, the pipe network upstream (i.e., all pipes that are involved in supplying water to a particular fixture) that was inserted by the designer or design engineer can assume the flowrate of that fixture. This can simulate simultaneous use and determine what the maximum expected flow rate is for a given time period. This simulation can be run over a predetermined time period (e.g., a 24-hour period) and can be repeated a sufficient number of times to generate valid data for the simulation. The maximum flow rate for each pipe in the network can be collected each trial, and a distribution of expected flow rates within pipes can be generated. Based on this information the designer / engineer can use desired velocities and pressure loss calculations to size the piping system of the structure / development.

[0026] The most important part of any sizing methodology is the data behind it. If an international demand estimation method were to be developed without considering the changes in water consumption, it would result in incorrectly-sized piping systems (i.e., piping systems that are not sized in an optimal or near optimal way based on dynamic demand). Such incorrectly-sized piping systems can cause excessive pressure losses and / or have detrimental health impacts, along with other problems. Embodiments of the subject invention utilize a stochastic process to predict water demand, such that randomly generated numbers are used to simulate realistic water consumption patterns based on data collected from real buildings. Water consumption and usage characteristics vary based on demographic and cultural factors. Embodiments of the subject invention provide a dynamic and holistic water demand estimation framework that includes influential factors and regional, cultural, and economic variables when predicting dynamic water demand in buildings.

[0027] Multiple factors can go into determining the simultaneous water usage in a building / structure / development. These factors are discussed in more detail as follows, along with how they can change. For most variables, each fixture has a distribution. Each variable can be represented with an equation, and each equation can be modified based on, for example, regions, demographics, and / or water heater type. Embodiments of the subject invention can be thought of as a “living framework”, ready to for periodic recalibrations as new data emerges / becomes available, without having to be reenvisioned.

[0028] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr Activation of Fixture: This can be represented by probability of arrivals given a certain time period. This can change by increasing or decreasing the number of expected arrivals in a given period.

[0029] Duration: This refers to how long the fixture is use when activated. Multiple distributions can be used to represent duration of use, but cumulative distribution functions (CDFs) functions would be the best fit. The curves can have translational shifts in mean, changes in distribution type (e.g., Weibull, lognormal, etc.), and changes in spread (e.g., standard deviation). This can be referred to as the duration parameter.

[0030] Ratio of Hot Water: While a fixture is active, determining the ratio of hot water can ensure that systems, fittings, and mechanical, electrical, and plumbing (MEP) equipment are appropriately sized. Each fixture can have a distribution. This can be referred to as the hot water ratio parameter. The ratio of cold water can be one minus the ratio of hot water.

[0031] User Patterns: Factors such as time wake-up time, departure time, return time, and sleep time can influence the concentration of water usage events. These can be simulated, and multiple distributions can be considered. These can have translational shifts (e.g., different wake-up / leave / retum time, etc.) and / or shifts in spread. This can be referred to as start time or the start time parameter.

[0032] Figure 1 shows a screenshot of a user interface (UI) that can be used with embodiments of the subject invention. The system can take into account several factors, including but not necessarily limited to typical household size, typical home value, expected population density, expected gender distribution, number of beds, number of baths, lot size, cost of water, and other factors. Based on the demographic and cultural factors (e.g., including age and household composition), projects can be sorted into consumption pattern groups (e.g., high consumption, medium consumption, low consumption).

[0033] Figure 2 shows a screenshot of a UI that can be used with embodiments of the subject invention, highlighting changes in the simulation between two different structures (labeled as “1” and “2” in Figure 2). Each group can have different equations that can be incorporated into the simulation to represent frequency, duration, percent of hot water, and user behavior patterns. The designer / engineer can be informed of the equations in the water consumption patterns section, where each of the equations can be shown. As more data is collected, additional consumption classes can be added and / or existing consumption classes can be modified. This can be done for design of multiple systems within the built environment, not just water demand. Any system that is dependent on user interactions can adopt this method.

[0034] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr Other methods and equations can be used other than the ones suggested in the distribution type category; these are just examples.

[0035] Figure 3 shows a schematic view of a setup on a structure (e.g., household) level, according to an embodiment of the subject invention. When predicting the demand, pressure losses are not considered only based on the type of fixture. The usage patterns of the fixture and the order of connection are also important. Using at least one machine learning (ML) algorithm, the floor plan can be read in, and fixtures and supply lines can be identified. For multi-story buildings, a global reference point can be used to connect floors, making the process easily applicable to multifamily units and large non-residential buildings. The lines that the designer / engineer lays out can be used to determine the connection order. From this connection order a network can be developed that is representative of the fixtures and their relationship.

[0036] Figure 4 shows a flow diagram of fixtures within a structure (e.g., a household, such as the one shown in Figure 3), according to an embodiment of the subject invention. Networks can comprise edges (pipes with an attribute that logs how much water is flowing through them) and nodes (points in the system where water is used (e.g., a fixture) and / or points where two edges connect). Each node can have defining attributes. The attributes can include the fixture type (e.g., lavatory, shower, clothes washer, etc.), the fixture count that displays the index of a given fixture (e.g., shower 1, shower 2, shower 3, etc.), the node number (e.g., how the simulation keeps track of respective nodes), the flow rate, the fixture state (whether it is active or not), and the remaining duration, which dictates how much longer a fixture will run for in the simulation. In the event of alternate water sources (e.g., graywater, rainwater, blackwater, etc.), an attribute can be added to show which system the node is on. A new network can be developed for each water stream.

[0037] Figure 5 shows a flow diagram of a simulation that was run using the fixtures shown in Figure 4. The pipes are labeled (e.g., E0, El, etc.). The simulation can comprise some or all of the following steps.

[0038] Step 1 : For each fixture group the number of daily events are generated using random distributions.

[0039] Step 2: Generate hot water ratio (subsequently cold water ratio by subtracting the hot water ratio from 1), duration, and start time of each event.

[0040] Step 3: Assign events to nodes, update current flow rate edges, and check to see if current flow rates are greater than the previously achieved maximum flow rate. If the current

[0041] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr flow rate is greater than the previous maximum, update the current flow rate as the new maximum.

[0042] Step 4: Once all events are assigned, collect maximum flow rates for components and store them (e.g., by writing to a file, such as a csv file).

[0043] Step 5: Rerun steps 1-4 until all trials are complete.

[0044] Step 6: Once simulation is done (i.e., after completion of step 5), the maximum flow rates can be used to size components.

[0045] Figure 6 shows results of the simulation depicted in Figure 5. Multiple trials were run to get a variety of conditions that may occur given the fixture layout. Each of the maximum achieved values for piping systems achieved was stored for each pipe. From the stored data, distributions were made for each pipe in the network. These distributions are assumed to be normal with a mean and standard deviation. These distributions of flow rates achieved in the many trials can be plotted onto the node network (as shown in the lower portion of Figure 6), and the designer / engineer can select the appropriate percent exceedance for a given pipe segment. It is traditionally set at 99% exceedance; however, this could change based on specific desires or needs. These flow rates can then be used to size the pipes (e.g., based at least in part on predetermined criteria).

[0046] Other components that affect hydraulic performance, such as backflow inhibiters / preventers or pumps, can be added to the node network, and the design criteria can be set at something other than the 99th percentile. These segments can be designed to the mean and checked for component specific failure criteria (e.g., based on design judgement of the engineer and / or regulatory standards).

[0047] Results from the simulation can then be fed into drainage and supply systems to simulate their loading capacity. These results can be fed into existing tools (e.g., AIRNET and / or DRAINET), which can simulate attenuation and air flow within these systems but fail to provide an accurate method for predicting sanitary loading. This can also directly work with Autodesk / REVIT by incorporating a plug in that could export all plumbing systems, run the analysis, and then return the appropriate sizes for the pipes. This could then automatically update the system in the Revit file. This program could also incorporate a searching algorithm by optimizing the pipe locations to reduce the amount of pipe needed for a building.

[0048] In addition, embodiments of the subject invention can be used as a validation tool to input modified drawings by builders to ensure as-built designs will function appropriately. This can be done by establishing the network but providing pipe sizes for all edges. Running

[0049] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr the simulation backwards could then determine if a contractor’s design is inadequate or adequate.

[0050] Data collected from the design process of embodiments of the subject invention can also provide metadata for a variety of purposes, such as fixture types, counts, and / or building layouts.

[0051] More precise ML algorithms can be used to develop a to-scale digital twin, which can be used to accurately size the piping system. This tool can simulate the system again and notify designers if the selected pipe sizes may experience excessive pressure losses or periods of severe stagnation that may lead to failure.

[0052] In an effort to take into account influential factors that in real life might lead to variations in user behavior, it can first be determined which factors have an impact on consumption patterns. High resolution data (e.g., data collected at a frequency of 1 Hertz (Hz)) can be used and can include flow rate data and demographic data (e.g., wealth, number of occupants, square footage, number of rooms, number of bathrooms, seasonal variations, etc.). It can be determined whether any of these factors (from the mentioned data) should be considered in design of residential water demand.

[0053] Instead, monthly billing data can be used to infer possible influential factors that may impact peak demand. This is based on the assumption that the monthly billing data is the equivalent of the integral over time of flow rate (i.e., a flowrate multiplied by a duration). With this in mind, monthly billing data and / or a method called geographically weighted regression can be used with embodiments of the subject invention to infer which factors / parameters may be influential on characteristics such as duration and frequency of use. This enables identification and quantification of influential factors that informs water consumption patterns and instantaneous water demand in buildings. There are no related art approaches for this application.

[0054] In one embodiment, the design process can flow as shown in the following outline.

[0055] Equation Definition: a) Designer (or design engineer) inputs location of project. b) Designer inputs the project type and occupancy. c) Machine takes project and runs simulation to determine the expected usage patterns of the building / structure to be constructed.

[0056] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr a. This process can utilize factors that are based on socio-economic data, building / unit characteristics, locational factors, cultural factors, and / or environmental factors (e.g., weather, water scarcity). b. Not all of these factors may be determined yet. c. Based on the location, certain factors that are behind the scene will populate, such as maximum allowable flow rate for each of the defined fixtures. d) The designer is then presented with a series of equations for frequency and duration. a. Intensity, and portion of hot water and cold water may vary based on the above factors. e) The designer will have the opportunity to change the equations, but these changes will have to be reviewed by the Authority Having Jurisdiction (AHJ) and will require supplementary datasets to provide evidence of these changes. f) The designer will then submit the equations and move to the network definition.

[0057] Network Definition: g) The designer has two options to define the network. a. The designer can upload floor plans or multiple floor plans to be read by ML and Al algorithms, which will identify pipes, fixtures, and equipment along with their relative location. i. In other water streams outside of hot and cold water may be incorporated, as can sanitary lines. ii. A search algorithm may be incorporated that can optimize the piping network costs. iii. For each water stream (other than hot or cold water), a new network will be developed. For example, if there is a water reuse system, a new network will be developed as the fixtures using reused water will only be supplied by the water reuse system.

[0058] 1. Sanitary systems utilizing different networks (e.g., graywater, blackwater) work using the same logic. b. The designer can enter in the nodes and edges one by one that are representative of fixtures.

[0059] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr i. Even if the designer uses the ML algorithm, the designer will still review all of the nodes and edges developed by the algorithm and modify the network. ii. In the event that joints are needed to accurately represent a system, they can be added. Joints will not have any activation equations or duration equations so they cannot activate. iii. Equipment can be added as well, and it also may not activate but will be sized using the flow calculated in the simulation. h) The designer will then submit the nodes and edges. The designer will continue to the next step.

[0060] Simulation: The simulation is shown in detail in Figure 7 and also described in detail in the Brief Summary section above.

[0061] Embodiments of the subject invention provide a focused technical solution to the focused technical problem of how to accurately develop and test pipe network sizing. The solution is provided by using a simulation to determine distributions of duration and frequency of use taking into consideration design parameters, as well as generating a layout of the pipe system using Al image reading tools. Designers and / or engineers can isometrically draw the pipe network based on the generated layout. The drawn pipe network can be used to design and / or build an actual pipe network.

[0062] The methods and processes described herein can be embodied as code and / or data. The software code and data described herein can be stored on one or more machine-readable media (e.g., computer-readable media), which may include any device or medium that can store code and / or data for use by a computer system. When a computer system and / or processor reads and executes the code and / or data stored on a computer-readable medium, the computer system and / or processor performs the methods and processes embodied as data structures and code stored within the computer-readable storage medium.

[0063] It should be appreciated by those skilled in the art that computer-readable media include removable and non-removable structures / devices that can be used for storage of information, such as computer-readable instructions, data structures, program modules, and other data used by a computing system / environment. A computer-readable medium includes, but is not limited to, volatile memory such as random access memories (RAM, DRAM, SRAM); and non-volatile memory such as flash memory, various read-only-memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories

[0064] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr (MRAM, FeRAM), and magnetic and optical storage devices (hard drives, magnetic tape, CDs, DVDs); network devices; or other media now known or later developed that are capable of storing computer-readable information / data. Computer-readable media should not be construed or interpreted to include any propagating signals. A computer-readable medium of embodiments of the subject invention can be, for example, a compact disc (CD), digital video disc (DVD), flash memory device, volatile memory, or a hard disk drive (HDD), such as an external HDD or the HDD of a computing device, though embodiments are not limited thereto. A computing device can be, for example, a laptop computer, desktop computer, server, cell phone, or tablet, though embodiments are not limited thereto.

[0065] When the term module is used herein, it can refer to software and / or one or more algorithms to perform the function of the module; alternatively, the term module can refer to a physical device configured to perform the function of the module (e.g., by having software and / or one or more algorithms stored thereon).

[0066] When ranges are used herein, combinations and subcombinations of ranges (including any value or subrange contained therein) are intended to be explicitly included. When the term “about” is used herein, in conjunction with a numerical value, it is understood that the value can be in a range of 95% of the value to 105% of the value, i.e. the value can be + / - 5% of the stated value. For example, “about 1 kg” means from 0.95 kg to 1.05 kg.

[0067] It should be understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application.

[0068] All patents, patent applications, provisional applications, and publications referred to or cited herein are incorporated by reference in their entirety, including all figures and tables, to the extent they are not inconsistent with the explicit teachings of this specification.

[0069] J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr

Claims

CLAIMSWhat is claimed is:

1. A system for developing a floor plan of a pipe network, the system comprising: a processor; and a machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following steps: a) receiving or determining edges and nodes within the pipe network; b) selecting distributions representing behavior of fixtures within the pipe network based on predetermined design criteria for the pipe network; c) generating a set of events by randomly sampling from frequency distributions of the fixtures using a Monte Carlo simulation; d) characterizing each event of the set of events by independently sampling behavior distributions of the fixtures to determine a duration parameter, a hot water ratio parameter, and a start time parameter; e) applying the set of events to the pipe network, wherein, for each event of the set of events, a flow is routed through the pipe network to generate an expected flow rate for each component of the plurality of components for a given timestamp in the Monte Carlo simulation; f) recording a maximum flow rate for each component of the plurality of components after all events of the set of events are applied; g) repeating steps b) - f) for a predetermined number of trials; h) upon completion of the predetermined number of trials, compiling the maximum flow rate for each component of the plurality of components; and i) selecting, as the respective design flow for each component of the plurality of components, the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, thereby generating the floor plan for the pipe network.

2. The system according to claim 1, wherein the instructions when executed further perform the following step:J:\UM\112XClPCT\\Application\Application - asfiled.docx\cri) adding the floor plan for the pipe network to a layout of a structure having the pipe network.

3. The system according to any of claims 1-2, wherein the instructions when executed further perform the following step: j) providing the floor plan for the pipe network to a design engineer to draw the pipe network isometrically.

4. The system according to any of claims 1-3, wherein the first function is a quantile function.

5. The system according to any of claims 1-4, wherein the instructions when executed further perform the following step: k) providing the floor plan for the pipe network to a design engineer to carry out pressure loss calculations and branch by branch pipe sizing computations for the pipe network.

6. The system according to any of claims 1-5, wherein the instructions when executed further perform the following steps: d-1) determining a number of events for each fixture for a single day; d-2) determining the duration parameter, the hot water ratio parameter, and the start time parameter for each event from step d-1); d-3) upon events being available to be assigned, assigning the events from step d-1) to the nodes; d-4) upon no nodes being available to be assigned, regenerating a new start time, and then reassigning the events from step d-1) to the nodes until each node has an event assigned thereto; e-1) upon all events being assigned, applying each event to upstream pipes and updating a flow rate for each component for each event; g-1) upon completion of step e-1), starting a next trial using the maximum flow rate for each component; and i-1) upon completion of the predetermined number of trials, utilizing the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, or fitting a distribution to an achieved flow of the pipe network, and thenJ:\UM\112XClPCT\\Application\Application - asfiled.docx\crselecting an exceedance probability to determine the respective design flow for each component.

7. The system according to any of claims 1-6, wherein step d) comprises using a plurality of influential parameters to determine the duration parameter for each active fixture in the pipe network.

8. The system according to any of claims 1-7, wherein step d) comprises using a plurality of influential parameters to determine the hot water ratio parameter and the start time parameter for each active fixture in the pipe network.

9. The system according to any of claims 6-8, wherein step d-2) comprises using a plurality of influential parameters to determine the duration parameter for each active fixture in the pipe network.

10. The system according to any of claims 6-9, wherein step d-2) comprises using a plurality of influential parameters to determine the hot water ratio parameter and the start time parameter for each active fixture in the pipe network.

11. The system according to any of claims 7-10, wherein the plurality of parameters comprises at least one of: relative wealth of an area having a structure having the pipe network; regional location of the structure; square footage of the structure; number of rooms of the structure; number of bathrooms of the structure; seasonal variations of the area having the structure; a size of a lot on which the structure is located; a cost of water in the area having the structure; ages of expected occupants within the structure; expected number of occupants within the structure; and expected composition of occupants within the structure.

12. A method for developing a floor plan of a pipe network, the method comprising: a) determining edges and nodes within the pipe network; b) selecting distributions representing behavior of fixtures within the pipe network based on predetermined design criteria for the pipe network; c) generating a set of events by randomly sampling from frequency distributions of the fixtures using a Monte Carlo simulation;J:\UM\112XClPCT\\Application\Application - asfiled.docx\crd) characterizing each event of the set of events by independently sampling behavior distributions of the fixtures to determine a duration parameter, a hot water ratio parameter, and a start time parameter; e) applying the set of events to the pipe network, wherein, for each event of the set of events, a flow is routed through the pipe network to generate an expected flow rate for each component of the plurality of components for a given timestamp in the Monte Carlo simulation; f) recording a maximum flow rate for each component of the plurality of components after all events of the set of events are applied; g) repeating steps b) - f) for a predetermined number of trials; h) upon completion of the predetermined number of trials, compiling the maximum flow rate for each component of the plurality of components; and i) selecting, as the respective design flow for each component of the plurality of components, the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, thereby generating the floor plan for the pipe network.

13. The method according to claim 12, further comprising: i) adding the floor plan for the pipe network to a layout of a structure having the pipe network.

14. The method according to any of claims 12-13, further comprising: j) providing the floor plan for the pipe network to a design engineer to draw the pipe network isometrically.

15. The method according to any of claims 12-14, wherein the first function is a quantile function.

16. The method according to any of claims 12-15, further comprising: k) providing the floor plan for the pipe network to a design engineer to carry out pressure loss calculations and branch by branch pipe sizing computations for the pipe network.

17. The method according to any of claims 12-16, further comprising: d-1) determining a number of events for each fixture for a single day;J:\UM\112XClPCT\\Application\Application - asfiled.docx\crd-2) determining the duration parameter, the hot water ratio parameter, and the start time parameter for each event from step d-1); d-3) upon events being available to be assigned, assigning the events from step d-1) to the nodes; d-4) upon no nodes being available to be assigned, regenerating a new start time, and then reassigning the events from step d-1) to the nodes until each node has an event assigned thereto; e-1) upon all events being assigned, applying each event to upstream pipes and updating a flow rate for each component for each event; g-1) upon completion of step e-1), starting a next trial using the maximum flow rate for each component; and i-1) upon completion of the predetermined number of trials, utilizing the maximum flow rate achieved for the respective component throughout all trials of the predetermined number of trials, or fitting a distribution to an achieved flow of the pipe network, and then selecting an exceedance probability to determine the respective design flow for each component.

18. The method according to any of claims 12-17, wherein step d) comprises using a plurality of influential parameters to determine the duration parameter for each active fixture in the pipe network.

19. The method according to any of claims 12-18, wherein step d) comprises using a plurality of influential parameters to determine the hot water ratio parameter and the start time parameter for each active fixture in the pipe network.

20. The method according to any of claims 17-19, wherein step d-2) comprises using a plurality of parameters to determine the duration of use for each active fixture in the pipe network.

21. The method according to any of claims 17-20, wherein step d-2) comprises using a plurality of influential parameters to determine the hot water ratio parameter and the start time parameter for each active fixture in the pipe network.J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr22. The method according to any of claims 18-21, wherein the plurality of parameters comprises at least one of: relative wealth of an area having a structure having the pipe network; regional location of the structure; square footage of the structure; number of rooms of the structure; number of bathrooms of the structure; seasonal variations of the area having the structure; a size of a lot on which the structure is located; a cost of water in the area having the structure; ages of expected occupants within the structure; expected number of occupants within the structure; and expected composition of occupants within the structure.J:\UM\112XClPCT\\Application\Application - asfiled.docx\cr

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